Merge branch 'features/include-full-file-in-convo-with-filter' of github.com:khoj-ai/khoj into features/include-full-file-in-convo-with-filter

src/interface/web/app/components/chatInputArea/chatInputArea.tsx

@@ -257,9 +257,6 @@ export const ChatInputArea = forwardRef<HTMLTextAreaElement, ChatInputProps>((pr
             setConvertedAttachedFiles(data);
         });
 
-        const totalSize = Array.from(files).reduce((acc, file) => acc + file.size, 0);
-        const totalSizeInMB = totalSize / (1024 * 1024);
-
         // Set focus to the input for user message after uploading files
         chatInputRef?.current?.focus();
     }
@@ -612,6 +609,7 @@ export const ChatInputArea = forwardRef<HTMLTextAreaElement, ChatInputProps>((pr
                 >
                     <input
                         type="file"
+                        accept=".pdf,.doc,.docx,.txt,.md,.org,.jpg,.jpeg,.png,.webp"
                         multiple={true}
                         ref={fileInputRef}
                         onChange={handleFileChange}

src/khoj/processor/content/docx/docx_to_entries.py

@@ -1,7 +1,5 @@
 import logging
-import os
+import tempfile
-from datetime import datetime
-from random import randint
 from typing import Dict, List, Tuple
 
 from langchain_community.document_loaders import Docx2txtLoader
@@ -94,26 +92,20 @@ class DocxToEntries(TextToEntries):
     def extract_text(docx_file):
         """Extract text from specified DOCX file"""
         try:
-            timestamp_now = datetime.utcnow().timestamp()
-            random_suffix = randint(0, 1000)
-            tmp_file = f"tmp_docx_file_{timestamp_now}_{random_suffix}.docx"
             docx_entry_by_pages = []
-            with open(tmp_file, "wb") as f:
+            # Create temp file with .docx extension that gets auto-deleted
-                bytes_content = docx_file
+            with tempfile.NamedTemporaryFile(suffix=".docx", delete=True) as tmp:
-                f.write(bytes_content)
+                tmp.write(docx_file)
+                tmp.flush()  # Ensure all data is written
 
                 # Load the content using Docx2txtLoader
-            loader = Docx2txtLoader(tmp_file)
+                loader = Docx2txtLoader(tmp.name)
                 docx_entries_per_file = loader.load()
 
                 # Convert the loaded entries into the desired format
                 docx_entry_by_pages = [page.page_content for page in docx_entries_per_file]
-
         except Exception as e:
             logger.warning(f"Unable to extract text from file: {docx_file}")
             logger.warning(e, exc_info=True)
-        finally:
-            if os.path.exists(f"{tmp_file}"):
-                os.remove(f"{tmp_file}")
 
         return docx_entry_by_pages

src/khoj/processor/content/pdf/pdf_to_entries.py

@@ -1,14 +1,10 @@
-import base64
 import logging
-import os
+import tempfile
-from datetime import datetime
+from io import BytesIO
-from random import randint
 from typing import Dict, List, Tuple
 
 from langchain_community.document_loaders import PyMuPDFLoader
 
-# importing FileObjectAdapter so that we can add new files and debug file object db.
-# from khoj.database.adapters import FileObjectAdapters
 from khoj.database.models import Entry as DbEntry
 from khoj.database.models import KhojUser
 from khoj.processor.content.text_to_entries import TextToEntries
@@ -97,26 +93,19 @@ class PdfToEntries(TextToEntries):
     def extract_text(pdf_file):
         """Extract text from specified PDF files"""
         try:
-            # Write the PDF file to a temporary file, as it is stored in byte format in the pdf_file object and the PDF Loader expects a file path
+            # Create temp file with .pdf extension that gets auto-deleted
-            timestamp_now = datetime.utcnow().timestamp()
+            with tempfile.NamedTemporaryFile(suffix=".pdf", delete=True) as tmpf:
-            random_suffix = randint(0, 1000)
+                tmpf.write(pdf_file)
-            tmp_file = f"tmp_pdf_file_{timestamp_now}_{random_suffix}.pdf"
+                tmpf.flush()  # Ensure all data is written
-            pdf_entry_by_pages = []
+
-            with open(f"{tmp_file}", "wb") as f:
+                # Load the content using PyMuPDFLoader
-                f.write(pdf_file)
+                loader = PyMuPDFLoader(tmpf.name, extract_images=True)
-            try:
+                pdf_entries_per_file = loader.load()
-                loader = PyMuPDFLoader(f"{tmp_file}", extract_images=False)
+
-                pdf_entry_by_pages = [page.page_content for page in loader.load()]
+                # Convert the loaded entries into the desired format
-            except ImportError:
+                pdf_entry_by_pages = [page.page_content for page in pdf_entries_per_file]
-                loader = PyMuPDFLoader(f"{tmp_file}")
-                pdf_entry_by_pages = [
-                    page.page_content for page in loader.load()
-                ]  # page_content items list for a given pdf.
         except Exception as e:
             logger.warning(f"Unable to process file: {pdf_file}. This file will not be indexed.")
             logger.warning(e, exc_info=True)
-        finally:
-            if os.path.exists(f"{tmp_file}"):
-                os.remove(f"{tmp_file}")
 
         return pdf_entry_by_pages

src/khoj/processor/conversation/utils.py

@@ -140,6 +140,35 @@ def construct_iteration_history(
     return previous_iterations_history
 
 
+def construct_chat_history(conversation_history: dict, n: int = 4, agent_name="AI") -> str:
+    chat_history = ""
+    for chat in conversation_history.get("chat", [])[-n:]:
+        if chat["by"] == "khoj" and chat["intent"].get("type") in ["remember", "reminder", "summarize"]:
+            chat_history += f"User: {chat['intent']['query']}\n"
+
+            if chat["intent"].get("inferred-queries"):
+                chat_history += f'{agent_name}: {{"queries": {chat["intent"].get("inferred-queries")}}}\n'
+
+            chat_history += f"{agent_name}: {chat['message']}\n\n"
+        elif chat["by"] == "khoj" and ("text-to-image" in chat["intent"].get("type")):
+            chat_history += f"User: {chat['intent']['query']}\n"
+            chat_history += f"{agent_name}: [generated image redacted for space]\n"
+        elif chat["by"] == "khoj" and ("excalidraw" in chat["intent"].get("type")):
+            chat_history += f"User: {chat['intent']['query']}\n"
+            chat_history += f"{agent_name}: {chat['intent']['inferred-queries'][0]}\n"
+        elif chat["by"] == "you":
+            raw_attached_files = chat.get("attachedFiles")
+            if raw_attached_files:
+                attached_files: Dict[str, str] = {}
+                for file in raw_attached_files:
+                    attached_files[file["name"]] = file["content"]
+
+                attached_file_context = gather_raw_attached_files(attached_files)
+                chat_history += f"User: {attached_file_context}\n"
+
+    return chat_history
+
+
 def construct_tool_chat_history(
     previous_iterations: List[InformationCollectionIteration], tool: ConversationCommand = None
 ) -> Dict[str, list]:
@@ -540,35 +569,6 @@ def get_image_from_url(image_url: str, type="pil"):
         return ImageWithType(content=None, type=None)
 
 
-def construct_chat_history(conversation_history: dict, n: int = 4, agent_name="AI") -> str:
-    chat_history = ""
-    for chat in conversation_history.get("chat", [])[-n:]:
-        if chat["by"] == "khoj" and chat["intent"].get("type") in ["remember", "reminder", "summarize"]:
-            chat_history += f"User: {chat['intent']['query']}\n"
-
-            if chat["intent"].get("inferred-queries"):
-                chat_history += f'{agent_name}: {{"queries": {chat["intent"].get("inferred-queries")}}}\n'
-
-            chat_history += f"{agent_name}: {chat['message']}\n\n"
-        elif chat["by"] == "khoj" and ("text-to-image" in chat["intent"].get("type")):
-            chat_history += f"User: {chat['intent']['query']}\n"
-            chat_history += f"{agent_name}: [generated image redacted for space]\n"
-        elif chat["by"] == "khoj" and ("excalidraw" in chat["intent"].get("type")):
-            chat_history += f"User: {chat['intent']['query']}\n"
-            chat_history += f"{agent_name}: {chat['intent']['inferred-queries'][0]}\n"
-        elif chat["by"] == "you":
-            raw_attached_files = chat.get("attachedFiles")
-            if raw_attached_files:
-                attached_files: Dict[str, str] = {}
-                for file in raw_attached_files:
-                    attached_files[file["name"]] = file["content"]
-
-                attached_file_context = gather_raw_attached_files(attached_files)
-                chat_history += f"User: {attached_file_context}\n"
-
-    return chat_history
-
-
 def commit_conversation_trace(
     session: list[ChatMessage],
     response: str | list[dict],